Abstract
Large-scale complex systems require high-fidelity models to capture the dynamics of the system accurately. For example, models of nuclear reactors capture multiphysics interactions (e.g., radiation transport, thermodynamics, heat transfer, and fluid mechanics) occurring at various scales of time (prompt neutrons to burn-up calculations) and space (cell and core calculations). The complexity of thesemodels, however, renders their use intractable for applications relying on repeated evaluations, such as control, optimization, uncertainty quantification, and sensitivity studies.
Original language | English |
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Awarding Institution |
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Supervisors/Advisors |
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Award date | 6 Oct 2020 |
Print ISBNs | 978-94-6421-022-4 |
DOIs | |
Publication status | Published - 2020 |
Keywords
- Proper Orthogonal Decomposition
- Locally adaptive sparse grids
- Greedy
- Nonintrusive
- Machine learning
- Uncertainty quantification
- Sensitivity analysis
- Molten Salt Reactor
- Large-scale systems